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Planning is a notoriously hard combinatorial search problem. In many interesting domains, current planning algorithms fail to scale up gracefully. By combining a general, stochastic search algorithm and appropriate problem encodings based on propositional logic, we are able to solve hard planning problems many times faster than the best current planning(More)
We report results from large-scale experiments in satis-ability testing. As has been observed by others, testing the satissability of random formulas often appears surprisingly easy. Here we show that by using the right distribution of instances, and appropriate parameter values, it is possible to generate random formulas that are hard, that is, for which(More)
It has recently been shown that local search is surprisingly good at nding satisfying assignments for certain classes of CNF formulas (Sel-man et al. 1992). In this paper we demonstrate that the power of local search for satissability testing can be further enhanced by employing a new strategy, called \mixed random walk", for escaping from local minima. We(More)
The Blackbox planning system unifies the planning as satisfiability framework (Kautz and Selman 1992, 1996) with the plan graph approach to STRIPS planning (Blum and Furst 1995). We show that STRIPS problems can be directly translated into SAT and efficiently solved using new randomized systematic solvers. For certain computationally challenging benchmark(More)
Part of the success of social networks can be attributed to the “six degrees of separation’’ phenomena that means the distance between any two individuals in terms of direct personal relationships is relatively small. An equally important factor is there are limits to the amount and kinds of information a person is able or willing to make available to the(More)
GSAT is a randomized local search procedure for solving propositional satisfiability problems (Selman et al. 1992). GSAT can solve hard, randomly generated problems that are an order of magnitude larger than those that can be handled by more traditional approaches such as the Davis-Putnam procedure. GSAT also efficiently solves encodings of graph coloring(More)